Objective In response to the issues of low accuracy in detecting small objects and insufficient target feature information in natural scenes for traffic sign detection a single shot multibox detector SSD algorithm using residual network ResNet and attention mechanism was proposed. The feature vectors extracted by residual networks and attention mechanisms were fed into a lightweight and efficient feature fusion module. Finally the output feature map was sent to the detector for detection thereby enhancing the accuracy of traffic sign detection. Methods Firstly the features were dimensionally reduced by 1×1 and then increased by 3×3 using residual modules and then the feature maps generated by the constant mapping and residual parts were summed pixel by pixel. Secondly the convolutional block attention module CBAM was introduced to the feature map output by Conv4_x of the residual module. Then the feature map output by the residual module Conv4_x and the feature maps output by the residual modules Conv2_x and Conv3_x were fed into the efficient feature fusion module for feature fusion. Finally the fused feature map was sent to the model for detection to realize the recognition of traffic signs. Results Through simulation experiments the improved SSD algorithm achieved an average precision of 90. 55% for detection on the Chinese traffic sign detection dataset effectively extracting feature information from small objects. Compared with mainstream algorithms including CenterNet YOLOv3 YOLOv4 Faster R- CNN and SSD the improved SSD algorithm improved the accuracy by 2. 57% 3. 4% 2. 79% 3. 8% and 4. 93% respectively. Conclusion The optimized object detection method extracts more feature information and achieves higher detection accuracy compared with other detection methods demonstrating good practicality and effectiveness in traffic sign detection.